@InProceedings{conf/edm/ZafraV09,
title = "Predicting Student Grades in Learning Management
Systems with Multiple Instance Learning Genetic
Programming",
author = "Amelia Zafra and Sebastian Ventura",
bibdate = "2010-10-06",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/edm/edm2009.html#ZafraV09",
booktitle = "Educational Data Mining - {EDM} 2009, Cordoba, Spain,
July 1-3, 2009. Proceedings of the 2nd International
Conference on Educational Data Mining",
publisher = "http://www.educationaldatamining.org",
year = "2009",
editor = "Tiffany Barnes and Michel C. Desmarais and
Crist{\'o}bal Romero and Sebasti{\'a}n Ventura",
isbn13 = "978-84-613-2308-1",
pages = "309--318",
URL = "http://www.educationaldatamining.org/EDM2009/uploads/proceedings/zafra.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.209.93",
abstract = "The ability to predict a student's performance could
be useful in a great number of different ways
associated with university-level learning. In this
paper, a grammar guided genetic programming algorithm,
G3P-MI, has been applied to predict if the student will
fail or pass a certain course and identifies activities
to promote learning in a positive or negative way from
the perspective of Multiple Instance Learning (MIL).
Computational experiments compare our proposal with the
most popular techniques of MIL. Results show that
G3P-MI achieves better performance with more accurate
models and a better trade-off between such
contradictory metrics as sensitivity and specificity.
Moreover, it adds comprehensibility to the knowledge
discovered and finds interesting relationships that
correlate certain tasks and the time devoted to solving
exercises with the final marks obtained in the
course.",
keywords = "genetic algorithms, genetic programming",
}